The Gambling Motives Questionnaire financial: factor structure, measurement invariance, and relationships with gambling behaviour
Bibliographic record
Abstract
Items assessing financial motives were recently integrated with the Gambling Motives Questionnaire (GMQ), resulting in a revised measure that assesses coping, enhancement, social and financial motives for gambling (GMQ-F). The aim of this research was to test the proposed four-factor structure of the GMQ-F, determine if GMQ-F responses were invariant across sex, and test a structural model that specifies links between motives, gambling frequency and problem gambling severity. Telephone surveys were conducted with 932 adult gamblers from across Manitoba, Canada, who responded to items from the GMQ-F and reported their frequency of gambling and levels of problem gambling severity. Confirmatory factor analysis yielded strong support for the four-factor structure of GMQ-F scores, and invariance testing provided evidence of measurement invariance across sex. Finally, support was found for the hypothesized structural model in which each gambling motive predicted gambling frequency, which in turn predicted problem gambling severity. Coping motives also directly predicted problem gambling severity. These results provide strong evidence in support of the validity of GMQ-F responses, offer further support for the integration of financial motives with the GMQ, and delineate relationships between gambling motives, gambling frequency and gambling-related harm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".